Cartographic palettes and colour harmonies

This story begins one day when I was assembling a map of the city of Edmonton, Alberta from OpenStreetMap data. It was going to be a big map, a 42″ (106 cm) wide poster for a wall.

OpenStreetMap colour palette

The data was good, but the standard OSM colours were not. They would work fine for a street map but, for a piece of art being seen all day in someone’s living space, there was an unpleasant amount of grey. Plus, the accents of pinky red and ochre, and the cold green parks, gave it a lack of colour harmony.

If you analyze the colour profile of this map, counting the number of pixels of each colour and assuming there are 12 colours being used, you can see that white and the first two greys occupy about 70% of the map surface. You’d not be wrong if you said, “That’s a grey map.”

Moreover, this colour palette does have any of the classic “harmonies” that we expect when we look for how colours work together in an image. (E.g., see here.) There’s no dominant point on the colour wheel, no complementary colour, no triadic distribution or analogous set. It’s not OpenStreetMap’s fault: this is just the colour mix that you get when you look at this specific framing of this city.

If we want the map to look harmonious, we need to think about two things. What colours are used? And what proportions are they used in?

For example, look at the 1986 map, below, of Central Asia from the Royal Geographic Society. My sense here is that the cartographers thought about the colours, their relationships to one another, and the percentages of the map they would occupy. Most of the colours are clustered around the green-yellow-orange side of the colour wheel, but there are a few complementary blues. There is enough green in India to showcase the yellows, oranges and browns of Tibet. The adjacent blobs of orange Tibetan highland and grey-brown Tarim Basin work well together. There’s a nice balance of values from very dark to white. There’s no particularly bright colour that draws the attention from other areas. It says “reference map,” rather than “map with a point to make.” Overall, it’s quite pleasant to look at.

Image Color Summarizer

It turns out that there’s an interesting tool for doing an initial analysis of a colour palette and the proportions of an image the different colours occupy. It’s called Image Color Summarizer and it comes to us courtesy of Martin Krzywinski at the BC Genome Sciences Centre. You go to http://mkweb.bcgsc.ca/color-summarizer/?home and go to the Analyze tab. Upload your image and choose, say, 12 colour clusters and very high precision.

The software then reduces the number of colours in the image to (in this case) 12 , using an algorithm that shifts colours a minimum to fit them into 12 categories. It then gives you all sorts of great information. So here we have first an analysis of colours in the RGS map of Central Asia…

…and then my OSM map of Edmonton:

This is very useful for a quick look at colour proportions and interrelatedness between colours. You get each colour in hex, RGB, HSV and LCH, and the percentage of the image that it occupies.

You might have noticed, however, that Image Color Summarizer gave different results on my OSM map from the ones I showed at the beginning of this page. It ranked the two greys first, and then white, with the three totalling only 60% of the image. There are a number of reason for this, but one is that this software does have a shortcoming: even at the “very high” precision setting it reduces your image to 200 pixels wide before doing its analysis. This means that if you have tiny features, like small labels or trim lines running alongside roads, their contribution to the overall colour mix can be lost.

If you want to use Image Color Summarizer fairly often, I recommend you download the software (on the Download tab) and run it locally at the command line. It’s a PERL script. This both reduces the load on the BCGSC server that hosts it, and allows you to analyze images larger than 200 px wide. You can just use your own computing resources and just let it run all night! (Think about it, though: an image 1500 px wide will take 100 times longer to analyze than one which is 150 px wide.)

At this point you might think the problem is basically solved: we extracted a 12-colour palette and their proportions from a map whose colour harmony I like, and all we have to do is to apply those to my OSM map.

But it’s not so simple. First of all, the OSM map did not have a 12-colour palette. In fact, according to GIMP (Color>Info>Colorcube Analysis), it had 213,078 colours. And this shouldn’t be surprising: OSM Downloader generated scores of rules for styling its many lines and polygon types. And then when I exported the map out of QGIS, antialiasing produced even more colours.

Just a few of the polygon styling rules from OSM Downlaoder

Image Color Summarizer suggested a palette, but before it did that it had scaled the map image to a mere 200 pixels across, and then simplified it to 12 colours.

Working with the actual map (some 12600 pixels across) there are two possible ways we can go:

  1. We could take the finished map with its OSM colours, and a “model” image, simplify them both to a reasonable number of colours (say 12), and then do a 1-to-1 substitution based on the order of colours when they are ranked by percentage of image. Or…
  2. We could simplify the styling of the map within QGIS to, say, 12 colours, and then apply the colours we get from simplifying the model.

Let’s look at each of these in turn.

Replacing the colour palette of a finished map, using GIMP

This is certainly the easier way, but it may not give you the level of control you want.

GIMP has an excellent tool for simplifying an image to a set number of colours at Image > Mode > Indexed. It allows you to decide how many colours you want (we’ll use 12), and whether these can be dithered or not. Generally I do not use dithered, except sometimes I do.

Once you have reduced your map to 12 colours, you can see those colours by opening the Colormap dialogue (Windows > Dockable Dialogs > Colormap).

Unfortunately they are not ordered by how much of the image they represent. You have to determine this yourself, and it’s a bit clumsy. Have the Histogram dialogue open (Windows > Dockable Dialogs > Histogram) and right click each colormap colour in turn, taking “Select this colour.” The Histogram window will tell you how many pixels in the image are selected. I take notes into a spreadsheet and that gives me the table I showed above:

The top line, “Index” tells me the position of the colour in the original colormap, from 0 to 11. I then re-order the colourmap in descending order of “real estate” (that is, what percentage of the image is occupied by each colour), by right-clicking any colour and choosing “Rearrange colormap.” Now my colourmap looks like this:

At this point, white, the most common colour, is index 0; the two greys are indices 1 and 2; two greens are indices 3 and 4; and so on.

I do the same tedious process with my model image. I reduce it to 12 colours, I make notes on how much of the image each colour represents…

…and re-order its colormap correspondingly:

In order to substitute the new palette colours for the original colours I have to do one more preparatory step. I have to convert this GIMP colourmap into a GIMP palette. I do this by opening the Palette dialogue (Windows > Dockable Dialogs > Palettes), right-clicking it and taking “Import Palette.” I import the palette using the model image as source.

Finally, I can apply that palette to the original map by going to Colors > Map > Set Colormap, and choosing the palette I just imported.

And there it is.

RGS “Mountains of Central Asia” colour palette

Now, you can see right away that this is both successful and not exactly what we expected. The map has acquired the palette of the original image, and by a stroke of luck the North Saskatchewan River became a pale green, which actually works for a river. It looks pretty good! But, to be picky, it doesn’t exactly have the original colour proportions. As noted before, the RGS map has quite a bit of green in it (anchoring the lower left), but we didn’t get that much green.

The reason for this becomes apparent when we compare the original map image (left) with its indexed version (right)…

Yes, there were a lot of green pixels there, but the indexing process turned much of it into patches of yellow. This is a hazard of reducing the number of colours in an image.

Still, that was successful enough that I want to try extracting a colour palette and proportions from some non-map image, and seeing if I can apply those to my map. Let’s try—I don’t know—this Georgia O’Keefe painting of an iris:

Once reduced to 12 colours (I did use dithering here) the palette in descending order of pixels looks like this:

And the result looks like this:

Georgia O’Keefe “Light Iris” colour palette

Again, it’s basically right, although the overall colour signature isn’t quite as light as the original image. The palette is centred around many varieties of purple and white, with a bit of nearby blue, opposed by a small proportion of complementary yellows. Looking at the original Georgia O’Keefe painting, I’d expect a bit more of the bright yellow, but I see that this colour went into the pixels of small features like riverside bike paths, only visible when you get up close. I’d also expect a bit more black, but the black got shunted into the railways lines so is quite fragmented and not visible when you look at the map as a whole.

This points out another factor: the extracted colour palette and proportions only tell part of the story of how an image presents itself. Another important factor is whether a given colour is fragmented into many small pixel groups, or collected into one or two large swathes. Maps (at least urban maps) tend to have a lot of pixels dedicated to small labels or street lines, and the colours used here rarely declare themselves when you look at the whole map.

Designing the map to a specific palette from the start

Swapping in the colours from an image with good colour harmony, in order of prevalence, sort of works, but now let’s look at our other alternative. This is simplify the styling of the map within QGIS to, say, 12 colours, and then changing these colours before exporting the map.

One advantage of this was that I would be able to apply a new palette to my map carefully. I wanted, for instance, to be able to put dark colours where I needed dark colours (labels, high contrast borders), to pair related colours together (parks and their sports fields, or neighbourhoods and their buildings) and to deploy complementary colours to occupy features that would allow them to give just the right amount of zing to the overall impression.

I also wanted to avoid this sort of thing, where the label and its halo in the original OSM map were of contrasting values, but after GIMP swapped in a different colour palette they had similar values.

Text was indexed colour “8” in the original map (left), which was a dark, warm grey. Its halo was colour “5”, a light tan, and this resulted in good contrast. In the new palette (right), colour “8” was a dark blue, and colour “5” was an only slightly lighter teal; as a result, there was very little contrast.

QGIS has an excellent feature for building a map from a specific colour palette: Project Colors. This is under Project>Properties>Default Styles. Essentially, you define a colour palette, each colour having a text string to identify it.

These palettes can be saved (and imported) as .GPL text files. Then, when styling a feature, you don’t assign it just any old colour: you use a data-defined override to assign it one of the colours from the Project Colors list:

With this in place, when the Project Color with that name changes, all features tied to it change colour as well.

Unfortunately, QGIS does not automatically pick up the colours of a project and list them for you. I still went through the process of indexing the image in GIMP and entering those colours as project colours. I named them by the typical features that used them. Note that once you begin assigning project colours to features, you can’t change the names of the colours (e.g., “building, street casings”) because in the QGIS project file the colours used in feature styling are actually referred to by these names.

It took quite a while to go through all of the styling rules and style everything using only the 12 project colours. Pleasingly, it did not change the overall look of the map very much.

Then I made an alternate project palette from the 12 Georgia O’Keefe colours, not worrying too much this time about what percentage of the original image they occupied, but assigning them where I thought they would work best. It seemed like it might be time to let go of applying colours in the order of their prevalence, because it was not necessarily producing good effects.

And that produced this map:

Georgia O’Keefe “Light Iris” colour palette, version 2

It has to be said that this method gives much better control. Given the colours you want in your palette, you can move them around to different classes of features until they represent the right proportion of the image. You can alter them slightly. You can save palettes you like and then read them back in later. (Note: when you read in a .GPL colour file, QGIS appends them to the project colours. Then you have to select and then delete the previous project colours .)

Furthermore, once the initial work of styling the map to a certain number of colours is done (and I soon expanded to 18 project palette colours, since 12 is pretty tight!), the process of going from a new model image…

“Early winter”

…to its palette…

…to a new version of the map…

“Early Winter” colour palette

… is quite fast. What made this quick was that, after GIMP indexed the photograph to 18 colours, I took a screenshot of its colormap, rotated that 90°, and then used the QGIS Colour Picker tool to assign these colours to the project palette. (And then saved that palette.)

A final piece of colour palette inspiration was this map from the Second Military Survey of the Habsburg empire, Galicia and Bucovina (1861–1864), which I obtained by screenshot from arcanum.com.

I love its muted but warm bronze tones, the pale green for flatlands, and the thin blue stream running through it. (This is the area today around Verkhovyna/Верховина, Ukraine). It also has a bit of complementary colour: some barely noticeable red elevation notations.

GIMP simplified this map to these 18 colours, which do capture the overall palette, but miss the red and the blue:

It seemed reasonable to just add them in:

And that produced this:

“Second Military Survey of the Habsburg Empire” colour palette

In summary

I’m sold on the second method: designing a map around a specific palette, and then swapping in alternate palettes with good colour harmonies stolen from other images. This was the approach that gave more satisfying results. You do more work up front, but in the process you also come to understand the underlying colour structure of your map: which colours are adjacent to which, which need to be related, which are fragmented and which occur in large continuous fields.

An 18-colour palette was much more reasonable than 12-colour palette. For this kind of urban map I calculated that I needed:

  • one colour with four different values. In the original map these were the greys: residential zones, buildings, neighbourhood labels.
  • other light-dark pairs, maybe six of these. For example you need a light blue and dark blue (bike routes and water), a light red and a dark red (motorway fill, motorway casing), a light yellow and a dark yellow (secondary road fill, secondary road casing), etc.
  • black
  • white

The GIMP is an invaluable tool in assessing the colours of an image. If nothing else, it can help you quantify the “colour signature” of an image.

If the overall colour harmony of your map matters (and it should!), palette-cloning and palette-switching are powerful tools. You could tweak your map so its colours reflect the natural colours of the area depicted. You could harmonize a map’s colours with those of the larger display in which it is being placed. You could deliberately echo the colours of an earlier map.

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Representing Timberline

When I taught map reading in the U.S., there was a piece of folklore that we used to tell our students: that the green areas on the USGS topographic map indicated there was a sufficiently dense forest there that you could hide a platoon of soldiers (about 40 people) per acre. It was a good way to explain why small clumps of trees (or “tree islands”) didn’t appear on the map.

This story lives on on the Internet, but there’s no evidence that it’s really true. I like the implied subtext — cartographers producing the maps for military officers involved in some kind of domestic war, and needing to know where they could hide their men from aircraft –- but it doesn’t take much reflection to realize that the U.S. Geological Survey never could have visited all those places, looked at the tree cover, and decided where you could or couldn’t hide 40 guys. Or even how to divide it up into suitable one acre blocks.

In Canada, the green area on the topographic maps has a specific definition. According to Natural Resources Canada it’s “An area at least 35 per cent covered by perennial vegetation of a minimum height of 2 m.” And they probably estimate that 35% coverage from air photos.

NRCan topo_sm

So, when you’re ascending a mountain and the green ends on the map, is that treeline, or is it timberline? I’d always thought these were just variant terms for the same thing, but then I read Jim Pojar’s book Alpine Plants of British Columbia. In the Introduction to this photo-rich handbook of all the plants you’re likely to see up there, I learned that treeline and timberline are different:

The term treeline designates the upper limit of the occurrence of tree species, regardless of their stature, whereas timberline refers to the upper limit of forest, of continuous cover of upright trees 3 m or more in height.

So timberline, being where the solid forest ends, is the end of green on our classic NRCan topos. Treeline is the last little, twisted, stunted tree.

Neither, of course, is really a line. As map scale decreases and you zoom in, the timberline becomes impossibly complex, and has to be generalized somehow. And no map, I think it’s fair to say, tries to represent treeline, since this would somehow be defined by many isolated clumps of krummholtz (“twisted-wood”, the bonzai-like tree clumps also known affectionately as shintangle) that you see after ascending past timberline. And just to make things a bit more complicated, as the climate changes it has become easy (in northern BC at least) to find areas above treeline where dozens of tiny seedlings are coming up and now surviving. How big would they have to be before one moves treeline?

Timberline however is a very important landmark for hikers and skiers, and how to represent it is a question that comes up frequently in topographic mapping. Of course using using generalized green and white is not the only option. I first learned this when I was bushwhacking across a 1:50,000 scale “provisional” series, black-and-white topographic map in northern BC. On these there is actually a black line that snakes across the elevation contours. It has “W” on one side of it (for wooded) and “C” on the other (for clear). Little pairs of tick marks pointed into the forested side. It was hard enough to read that I took a pencil crayon and shaded in some green on the W side.

NRCan BW topo_sm

There’s also the solution that National Geographic used years ago in mapping northern Canada; the Northern Limit of Wooded Country is represented by a line of tiny tree symbols.

NatGeo_sm

Sometimes using green is just out of the question. If you want to use a range of colours to represent elevation (hyposgraphic tinting), having green forest is going to be quite confusing. In these cases I have I tried a technique of having a dashed green line at timberline, bordered by some fill on the downhill side, fill that quickly fades out. You can do this in QGIS by using shapeburst fill, shading to a set distance of a few millimetres.

McDonell_Lake_detail_sm

I have also played around with not marking timberline at all, and just putting a note beside the trail at the point when one would clear the trees.

OpalRidge_sm

Another option available to you if you have access to landcover data, is to give map-readers more information about how the forest makes that complicated transition to grassy tundra. In this case I used the Land Cover, circa 2000-Vector data available at Geogratis, and assigned progressively lighter colours to “coniferous dense” (which captures the main forest of spruce and fir), “coniferous open” and “broadleaf open” (which captures willow).

Seaton_sm

If timberline is something you just want to suggest, but don’t really need to accurately show, a method that can still look good is to style the digital elevation model in a series of fading greens. Have it become completely white at the elevation where timberline is typically encountered in the area. You get the effect of the “naked” mountains rising above forested slopes without introducing the complexity of avalanche tracks and the differences in where trees grow between north- and south-facing slopes.

RedRose_sm
Timberline in this area tends to be at 1500m

Sometimes the highest elevations on the map just touch timberline, in which case there can be a large zone where small meadows alternate with clumps of diminutive trees, an ecology often called parkland. I’ve tried representing this by scattering a host of little tree shapes, sort of trying to show how trees are still there, but not connected to the “mainland” of the forest.

QuickHills_sm

And this is just scratching the surface. The more you study timberline, the more you realize the folly of trying to show it accurately, yet the importance of indicating where we see it!

 

 

 

A new map of the Bulkley Valley

When I attended the ICA Mountain Cartography Workshop in Banff last spring, I was encouraged by a number of people there to consider printing and selling maps of my local area. Most of this last winter has been taken up by the production of this map, which was printed in April and is now available at Interior Stationary in Smithers, BC.Bulkley_Valley_poster_map_wordpressThere are a number of goals coming together here. From a cartography perspective, the idea was a map that would feature rich shaded relief generated in Blender. From a community perspective, the idea was to produce a map that represents the amazing topography of this area, that draws you in and makes you want to go exploring.

Every map is a story, and maps, being deliberately authored, contain bias. In this case, I wanted the story to be about the terrain — the mountains, rives and lakes — and my bias was to emphasize the romance of landscape at the expense of the technical ways we divide it up. So missing from this map are all those lines that chop up a landscape: town boundaries, ski area boundaries, recreation site boundaries, private land designations, First Nations reserves and Regional District subdivisions. Even provincial park boundaries got the axe, because while on the one hand a park indicates a piece of the landscape to be preserved from development, on the other hand is the implication that outside the boundary anything goes. Park boundaries on maps suggest that only within the line is the higher quality landscape. I wanted to avoid that suggestion.

As a concession to practicality, most roads are named and you can use it as a road map. But it’s not one of those maps where every road in the provincial database is present. I left out the vast majority of forestry roads, including only those mainline roads which are commonly driven. And here too bias plays a part, because although I’ve lived in the valley for 20 years, my notion about what roads are “commonly driven” will differ from someone else’s.

Bulkley_Valley_poster_map_detail2_wordpressAs another concession I put in some point markers for those small provincial parks, or recreation sites, where a person can camp or picnic. I did this because, like roads, these are important landmarks for people.

Trails are not shown on the map, partly because at this scale, 1:150,000, you can not use it as a trail map, but also because I didn’t want to suggest that trails are an important interpreter of the landscape. When you put trails on a map they stand out as a travel network, perceived in the mind not unlike a road network. We think that where trails go must be better than where trails don’t go. I didn’t want to bias the viewer in this way.

The colouring of the map is derived from land cover data, which is essentially a satellite or aerial photo that someone has looked at and classified into zones of different stuff. Coniferous and deciduous and mixed forest are all identified, as are ice and snow, bare rock, built-up areas and shrubland. I’m a big proponent of the idea that a map should faithfully give you the colours of a landscape before you ever arrive in the area, so the colours I chose were keyed to the summer landscape here — and consequently there’s a whole second possible project of a winter map of the valley. I think the colours work pretty well, although it’s something of an idealized summer landscape. For example, there are no lingering snowpatches such as one would actually see. It is quite clear how coniferous forests dominate upper slopes while the valley bottoms are full of cottonwood and aspen. You see how south facing slopes are different from north-facing. Given that it can’t account for how light changes throughout the day, it’s a pretty good representation of what we see.

Bulkley Valley detail 300dpiNaming is an interesting area in map-making. If you publish an article about how such and such peak should be named after your uncle Fred, people will agree with you or not, but you’ll be known as having proposed that name. However if a map-maker puts a name on a mountain or creek, no one really questions it. Maps are seen as authoritative.

But names can be seen as impositions on the landscape like other lines. Does it improve this river that we call it the Bulkley River? Would it be a better river if it was labelled the Wedzenkwe? Or is it best not labelled at all? I made a decision to go with the naming of features that reflects the British Columbia geographic names database — in other words, the names that appear on most maps and with which most map-readers are familiar — but I’m not entirely comfortable with this. For example, I’d like to see a version of this map with Wet’suwet’en and Gitxsan names on as many features as possible.

We have a lot of unnamed features in this area — or, I should say, features without official names. I hope that this map will act as a lightning rod for name information, that people will give me local names and I can add them on to future editions, and thus substantiate them.

Buffering (or haloing) text over complex backgrounds using the Screen blend mode

This is a common problem when you are working over a complicated, multi-colour background. Such backgrounds tend to swallow text!

Here’s an example, where text lies over a background with a lot of variability in value (darkness).text over complex background

Here’s the same image in greyscale, just to show the pixel values. Out of a total range of 0 to 255, the pixels here range from 6 (almost black) to 240 (almost white). Black text over light portions reads easily, but it’s hard to read the letters, M, N and T because of the way they overlap dark areas.value map of background

The classic, or perhaps I should say default solution, is to buffer the text in white. This works fine, in that you can read the text more easily, but it’s a crude solution and it’s a bit loud. It lacks subtlety. white 1mm buffer

A somewhat more elegant solution is to have the buffer match the background colour. In this example the text stretches across many background colours, so no matter what colour you select it’ll be a compromise. Still,  it looks better than white. The problem is that across the map as a whole each piece of text will probably get a different buffer colour, depending on what is dominant in its background.1mm colour buffer

What we really want is a way to pick up the background colour for each pixel, and colour the buffer that way. We can actually approximate this by using the blend mode of Screen.

Screen is the opposite of Multiply. Where Multiply transfers dark values onto another layer, Screen uses dark values to lighten another layer.

In this case, you set your buffer to something fairly dark, like 80% grey. Then set the blend mode of the buffer to Screen. You get this.screen 80 percent grey

Here the buffer colour echoes the underlying colour. The buffer around the T is orange-y, while the buffer around the O is grey. The buffer for the M uses both colours.

How would you do this in QGIS and Inkscape?

For text labels displayed in QGIS, this impressive piece of software can do this automatically. On the Buffering tab of the Labels dialogue, check Draw text buffer and specify an 80% grey for the buffer fill. (Note that 80% grey is RGB 51/51/51, HSV 0/0/20, or HTML #333333.)

Then set the blend mode to Screen. There are a couple things to note. One is that the sample shown will look ghastly; you can ignore that. The other is that the blend mode of the label is different from the blend mode of the buffer. The label itself probably has the default blend mode of Normal, and this can be checked on the Text tab.QGIS_buffer_screen

Here’s the result: Tiltusha example

In Inkscape, where you might be doing more complex text, text set on curved paths with adjusted kerning, you can also do this. Blend modes in Inkscape are applied to an entire layer, so you can’t give one piece of text a different blend mode than another. But by introducing a special layer that has a blend mode of Screen just beneath your text, you can put the buffers there.

The procedure goes something like this:

  1. Create a new layer below your text layer and set its blend mode to Screen.
  2. On the text layer, select your text and duplicate it with Ctrl-D. duplicate
  3.  Turn on Stroke for the duplicated text. (Text normally has fill but no stroke) and set the stroke colour to 80% grey. (You can do both of these steps simultaneously with a shift-click on the 80% grey swatch in the Palette at the bottom of the screen). Set its width as you see fit (1mm in this example). It should look pretty ghastly.buffer stroke on
  4. Press Shift-PgDown to send that duplicate piece of text to the layer below, the layer with blend mode of Screen. It should now look pretty nice. buffer moved to screen

I should point out one disadvantage of this two-layer approach: you can’t group the text with its buffer, a practice I normally do because it makes it easier to move labels around later.

Shaded relief with BlenderGIS, part 2

With the DEM read in, we’re now ready to adjust the many parameters in Blender which determine what we’ll get. Here again is the overall procedure (mentioned in the previous post) . We’re now at step 5.

  1. Select the Cycles Renderer
  2. Read in the DEM as DEM
  3. Create new material
  4. Adjust Z scaling
  5. Create and adjust the georef camera
  6. Correct final pixel dimensions to match the input DEM
  7. Adjust lamp location and properties
  8. Test render
  9. Adjust final parameters
  10. Render

Create and adjust the georef camera

With the plane selected, go to the the (new) GIS tab on the left, and, under BlenderGIS, click Georender.

geoRender.jpg

This creates a camera at an appropriate distance above the plane. It will appear in the tree of objects (upper right) as “Georef cam”. It has been placed high enough (Z value of 16791) that it is above the highest point on the plane surface. Note that it is important to have set your Z scaling before creating the Georef cam.

georef cam set up

If you look at the camera’s Object Data>Lens, you’ll see that BlenderGIS has automatically set it to Orthographic. It’s a good idea to note the camera’s clipping Start and End, because you may have to adjust these later.

geoRefCamDetails

As well, if you check out the Scene’s Scene panel, you will see that the current camera has been set to this new Georef cam.

Correct final pixel dimensions to match the input DEM

If you go to the Render tab, under Dimensions>Resolution you can see the pixel dimensions of the final image as suggested by BlenderGIS. In this case, as I noted before, one cell has been dropped in each dimension, so it is 5029 x 3276.

resolutionInitial

I correct these to the actual values (5030 x 3277, in this case) so that the dimensions of my final image match the DEM. That way I can pair the TFW world file with the image created by Blender, and read it into QGIS georeferenced.

dimensionsRepaired

While I’m at it, I’ll set the Resolution percentage to 25%. This way I can get a test image without doing a full render. While I’m here on the Render tab, I’ll go to the Light Paths panel and set it to Limited Global IlluminationlimitedGlobalNow we’ll check that all elevations in the DEM are visible to the camera. Pop into Camera Ortho view (Numpad 0 while the mouse is over the 3D View). You are looking for  black faces where the higher parts of the DEM have been cut off. It can look something like this:orthoView1BlenderGIS was updated in December 2014 to place the Georef Cam high enough so that this typically does not happen (Thanks!), but I’ll still discuss what to do in case you do encounter it. This could happen if you, say,

  • create the Georef cam before you increase Z scaling on the DEM.
  • create the Georef cam at a time when the View number in the Subsurf modifier is low (e.g., 6). In this case the displacement is quite generalized; the Georef cam will be placed high enough to clear it, but not the actual high points that pop out when you render at 11.

What’s happening here is that the camera only “sees” objects that fall between the Start and End clip distances we noted a few steps back. The Start and End are measured from the camera outward (or down, in this case).

Specifically, in the image above, I had a camera at 11,706m, with a Clip Start of 0.5 m, and a Clip End of 8011.5 m. If you do the math, you’ll see that this means that the maximum elevation at which it can see objects is 11,705.5 (that’s 11,706 – 0.5). The minimum elevation at which it can see objects is 3694.5 (that’s 11,706 – 8011.5).  Our DEM originally contained elevations from 739 to 2379, but now we’ve scaled it up 5 times, so its minimum is 3695 (= 739 x 5) and its maximum is 11,895 (= 2379 x 5).  The black patches are the elevations that are higher than 11,705.5 m.

If I do a test render with these settings I’ll get the image below; I’ve circled the black patches where the highest peaks were cut off.SRTM_2_3_cutoff_render

In this case we need to raise the camera to just above the max value in the DEM, and then adjust the Clip Start and End distances to encompass the whole DEM. Since the DEM ranges from 3695 to 11,895, let’s put the camera at 11, 896, leave Start at 0.5, and set End to 8201.5.

 Adjust lamp location and properties

This does not differ in any way from what you would do with the Subdivide method. Set the Lamp to Sun, and click Use Nodes. I like a sun size of 1, Cast Shadows checked, and a Strength of 4.

lampSettings

Then change to the Lamp’s Object tab, Transform section, and set its X, Y and Z Rotation. In this case I want an elevation of 40°and an azimuth of 337.5°, so I choose X=0, Y=40, and Z=112.5. Remember that Blender measures the sun azimuth counterclockwise from East. lampSettings2

 Test Render

Hit F12 to see a test render at 25%. It should look grainy but basically correct.

Final adjustments

At this point you can play with:

  • lamp rotation, strength, size
  • Z scaling on the model
  • the Render number under the Subsurf modifier on your plane

When you’re ready to go,

  • On the  Render tab>Dimensions, raise the Resolution from 25% to 100%
  • On the  Render tab>Sampling set the Samples Render number to 200. This high sampling number makes the rendering process take a long time, but it’s worth it. Check out this comparison of the same scene rendered with sampling 200 (left) vs sampling 90 (right):

    Note the grain
    Note the noise in the shadows when sampling only 90 times (right image) as opposed to 200 times (left image).
  • On the Render tab>Output, set the image type to TIFF (if you want to pair it with a pre-existing TFW after you render)
  • Save your session to a .blend file using File>Save As…

Render

Two options here. One is that you can hit F12 and go out for coffee.  When it’s done, hit F3 to save.  Or, you can close Blender and render that .blend file you just saved from the command line. This saves the GUI rendering, and so it takes less time — you might get 10-40% off the regular render time.

Let’s say your  .blend file is called tile_4_4.blend. The command line to render it would be

/path/to/blender -b tile_4_4.blend -o hillshade_4_4.tif -F TIFF -f 1

The switches are:

-b     run in background (no-GUI) mode
-o     the output filename
-f 1    render just one frame.

Pulling the result back into QGIS

Let’s say you have saved the rendered image as hillshade.tif. Rename a copy of the world file you created way back at the beginning to hillshade.tfw.

QGIS should be happy to read in hillshade.tif now as raster data, although it will probably ask for the CRS. (You know the CRS because you specified it when you re-projected the DEM, and when you read it into Blender.)

It works quite well in QGIS to set the Blend Mode of the hillshade to Multiply, and then overlay it with layers representing vegetation or elevation.

The shadows can be thunderingly deep in these hillshades…

QGIS1

…so it can be good to display them with increased brightness and decreased contrast.

QGIS2

 

Shaded relief with BlenderGIS, part 1

Note: This tutorial has now been superseded by a new one which uses Blender 2.8.

[The BlenderGIS add-on has now been improved to the point where generating shaded relief is downright easy. So, I have updated this post (April 2017) to reflect how one uses its latest version. Also the latest version of Blender (2.78c).

You may have read Daniel Huffman’s great tutorial about created shaded relief using the free, open-source animation software, Blender. This is an update of Daniel’s video tutorial for his method.

In Daniel’s method, you subdivide a plane to create a grid of finely spaced points. You then apply a Displace modifier to the plane with a DEM, and get some very realistic results.

There are a few things about this method, which I’ll call the Subdivide method, that were difficult for me. For one thing my meager 4GB of RAM precluded subdividing the plane more than about 1500 times. So I went searching around, and found to my surprise that there’s a plug-in available for Blender called BlenderGIS that does much the same thing. But unlike the Subdivide method, BlenderGIS uses a Subsurf modifier. This saves a lot of memory.

Note that distinction: subdivide vs. subsurf. That’s important

As well, BlenderGIS offers a few interesting tools for the GIS user. One is the ability to create a GeoRef camera, correctly configured and positioned to shoot an image of your lit DEM. Another, which I haven’t really explored, is the ability to read in shapefiles to add features to the DEM surface.

In this series of posts I give a tutorial for how to use Blender GIS’s subsurf method to achieve results similar to those obtained with the subdivide method.

I’ll assume that you have watched Daniel’s tutorial, so finding your way around Blender for purposes of generating a shaded relief isn’t too hard for you; and this of course implies, as well, that you are the kind of GIS user who knows what a DEM is. My workflow mostly happens under Ubuntu 14.04 using QGIS and GDAL tools.

We’ll do this in two parts. Part 1 is about installing Blender GIS,  preparing your DEM, reading it in and adjusting the vertical exaggeration. Part 2 is about creating the georef camera, placing the lamp and adjusting some final parameters.

Installation of BlenderGIS

The master instructions are in the BlenderGIS wiki at https://github.com/domlysz/BlenderGIS/wiki/Install-and-usage  However, I will paraphrase them here.

Go to the BlenderGIS site on github and hit the Clone or Download button. You will receive a .zip file, which you can store pretty much anywhere.

Within Blender,  at File>User Preferences, the Addons tab, click Install From File, and point it at the .zip file you just downloaded. (You don’t have to unzip it.)

Once its installed, you still have to do two things:

  1. Check the box next to 3D view: BlenderGIS to enable it
  2. Click Save User Settings

That’s it. BlenderGIS is installed.

Preparing your DEM

I used to use divide my DEM up into square tiles, and feed these one by one through Blender. However BlenderGIS in no way requires this.

Projection and resolution

BlenderGIS claims that it can now read a DEM projected in “WGS84 latlon”, but I have not had very good results with this. I still prefer a DEM that is in a projection that uses metres. Typically for me this would be a UTM projection.

You may also want to re-sample your DEM, so the cell size is appropriate to the scale of the map you are making.  I use this guideline: the DEM resolution should be map’s [metric] scale divided by 10,000. For example, if the map scale will be 1:100,000, I want a DEM with 10 metre cells. For a 1:30,000 scale map, I want a 3 metre DEM. It’s just a general guideline.

Before you re-sample and re-project, however, be sure to float your DEM and create a world file.

Float DEM

Most DEMs come as integer values. Floating it means converting it to a DEM that can has floating point elevation values.  You can do that with a line like this with gdal_translate.

gdal_translate -ot Float32 myDEM.tif myFloatDEM.tif

where the -ot switch specifies output type.

World file

You’ll need the world file when you are done, to georeference your hillshade.

Almost all DEMs come as GeoTiff files, and the corresponding world file extension is .tfw. An easy way to create a TFW World file is with the GDAL command gdalwarp:

gdalwarp -co "TFW=YES" myGeoTiff.tif deleteme.tif

This just makes a second copy of myGeoTiff.tif called deleteme.tif, but in doing so it creates a world file called deleteme.tfw. (The switch -co “TFW=YES” means to use the creation option “TFW=YES.”) The world file is what you really want, so delete the deleteme.tif. The world file and the main image file need to share the same name, so rename the TFW to myGeoTiff.tfw.

Re-sample and re-project

I like to do these two operations simultaneously with gdalwarp. In the following example, I am re-projecting out of 4326 (the EPSG code for lat/long/WGS84) into 32609 (the EPSG code for UTM Zone 9 north/WGS84).

Where

-s_srs
the Spatial Reference System (projection & datum) of the original
-t_srs
the SRS of the result
-r
resampling method
-tr
resolution, in metres
-of
output format
gdalwarp  -s_srs EPSG:4326 -t_srs EPSG:32609 -r bilinear -tr 5 5 -of GTiff myFloatDEM.tif myFloatDEM_32609_5x5.tif

Note that the terms “projection/datum”, SRS (Spatial Reference System) and CRS (Coordinate Reference System) are interchangeable for our purposes.

Now we’re reading to open up Blender and get started.

Overall process

The process will essentially go like this (and you can use this as a checklist once you get familiar with it):

  1. Select the Cycles Renderer
  2. Read in the DEM as DEM
  3. Create new material
  4. Adjust Z scaling
  5. Create and adjust the georef camera
  6. Correct final pixel dimensions to match the input DEM
  7. Adjust lamp location and properties
  8. Test render
  9. Adjust final parameters
  10. Render

The green steps are probably already familiar to you from Daniel’s tutorial.

Select the Cycles Renderer

Open up Blender and delete the default cube that appears. Switch the selected renderer to the Cycles Renderer.

Cycles

Reading the DEM in

File>Import> Georeferenced rasteropenDEMAsPlane

Select your floated, re-projected, re-sampled DEM, and in the left margin, where it says Import georaster, set Mode to As DEM, Subdivision as Subsurf, and select the correct CRS for your DEM.

importGeoraster

If the CRS you want to use is not on the dropdown menu,you can add it by clicking the “+” button, checking the Search box, typing in the EPSG code for your CRS into the Query box, and hitting Enter. It should appear in the Results box, and then you just click OK.

addingANewCRS

With all these things set, you click Import Georaster, and the result is a somewhat flabby-looking, grey plane.

flabby-looking grey plane

Tap n (with the cursor over the 3D view) to bring up the 3D view numeric panel, and we’re ready to go for a tour of things that BlenderGIS has done automatically. You don’t ordinarily have to check these things, but they are worth knowing about.

  • On the 3D view numeric panel we can see that the dimensions of the plane are the real-world dimensions of the DEM. In this case I’m using a 5030 x 3277 DEM with 5 m cells. For some reason BlenderGIS drops one cell in each dimension, so the DEM is 5029 x 5 on the X dimension (25145), and 3276 x 5 on the Y dimension (16380). metres on a side. We also see the thickness (Z dimension) of the DEM, which is 1911.56 metres.

transfromPanel

  • Also on the 3D View Numeric Panel, under Display, we can see adjustments to the grid floor. It gives values for Lines and Scale. Ordinarily (e.g., with the default cube) these will be values like 16 lines and a scale of 1. This means lines extend out 16 from the origin and no more, and each square is 1. After BlenderGIS has read in a DEM,  these can be more like 25 lines with a scale of 1000. In this case we have 25 lines on each side of the origin and each represents 1000 units (corresponding to metres).

gridFloor

  • Again on the 3D View Numeric Panel, under View, the clip distances represent the nearest and farthest you can see the object in perspective view. Ordinarily (e.g., with the default cube) these will be values like 0.1 to 1000. With a DEM read in these can be more like 0.1 to 250,000.

clipDistances

  • No longer on the 3D View Numeric Panel, but under Scene>Custom Properties, BlenderGIS has added a crs X and crs Y which correspond to the UTM coordinates of the centre of the plane. This is how the plane can be centred at (0,0), but any additional georeferenced data placed over it will be in the correct place. There’s also an SRID, and even a latitude and longitude.

customProperties

To understand how the DEM was used to displace a plane, go to the Modifiers tab for the plane. You will see two modifiers have been created. The upper one, called DEM, is a Subdivision Surface, or Subsurf. This kind of modifier subdivides the plane on the fly, and you can specify separate subdivision factors for View mode (what we’re doing right now) and Render mode (when you render the image). The beauty of the Subsurf modifier is that you can leave the View mode number low (e.g., 6) but set the Render number high (e.g., 11).

The factor is used as an exponent of 2, so our View mode is 2^6 or 64 subdivisions along the side of the plane. This is a small number considering that the DEM itself is thousands of points on a side. Which is why the view looks lumpy. The second modifier, called DEM.001, is a Displace modifier, and it applies the DEM to the subdivided plane much as in the Subdivide method.

Try changing the View number on the Subsurf modifier, and watch the fineness of the plane increase. You’ll notice that it takes longer to adjust the 3D view each time you increase the View number by 1, because you are doubling the number of subdivisions. A view number of 6 corresponds to 64,  7 to 128, 8 to 256, 9 to 512, 10 to 1024 and 11 to 2048 subdivisions. increasingViewNumberBy the time you get to 10 or 11, you’ll notice the status bar at the top of Blender is reporting a lot more memory use, 1.2 GB in this case.

statusBarat11

Go ahead and set the View number back to 6, but increase the Render number to 10 or 11. Because this higher number of subdivisions will be performed on the fly during render, it takes no memory now.

Create new material

If I now go to the Material panel for the plane, I’ll see that at this point I have no material.

noMaterial

I click New to create a new one. I get the default Diffuse BSDF  material, with a roughness of 0. This is fine, and if I want I can change the material or some of its parameters later.

defaultMaterial

Adjust Z scaling

Last, we’ll adjust the Z scale factor in the 3D View Numeric Panel to reflect the vertical exaggeration we want. I’ll use 5 in this case. Note that the Z dimension of the plane jumps up to 5 times its previous value, to 9794.137 in this case.

ZScaled

Go on to Part 2.

Cutting large DEMs into square, partially overlapping tiles

I’ve been doing a lot of shaded relief using Blender, and one practice I’ve developed is feeding Blender square DEM tiles.

The DEMs I use in maps tend to be too big for my installation of Blender to handle. One I had recently was 6348 x 10899 pixels; another was 4155 x 3572.  So that’s one reason for tiling them: I can produce shaded relief of the tiles separately in Blender and then mosaic the results.

And as long as I’m tiling, I’d like to use square tiles. That’s because when you subdivide a plane in Blender you have to use the same factor on both X and Y axes. Call me superstitious, but I get uncomfortable knowing that I’ve mapped 1803 pixels of DEM onto 1500 cells along one axis, and 2403 pixels to the same 1500 cells on the other. It seems to me this should cause loss of precision.

By feeding Blender square tiles, I eliminate that. I can subdivide the plane 2048 times on both axes, and feed it a 2050 x 2050 DEM.

This is not the kind of tiling that you would use a tool like gdal2tiles.py to do. That tool produces tiles that do not overlap, but I want some overlap. This is for a couple of reasons.

One is that Blender sometimes produces a bit of an edge artifact on a tile’s down-light edge (that is, the edge of the tile away from the light source). I can trim this off, and if there’s a bit of overlap between adjacent tiles, I get a nice smooth mosaic.

Left: mosaic of two non-overlapping tiles -- note the "seam" down the centre. Right: the left tile trimmed, and the right tile (with an overlap of 200 pixels) laid under it. Light is from 337.5°
Left: mosaic of two non-overlapping tiles — note the “seam” down the centre. Right: the left tile trimmed, and the right tile (with an overlap of 200 pixels) laid under it. Light is from 337.5°

Another reason for overlap is that if a shadow-casting feature (a mountain, a cliff) is just off the up-light edge of a tile, Blender can’t know to cast the shadow from that feature onto the tile. Overlap cleans this up by putting a healthy margin of features on the up-light side.

How much you overlap seems to depend on the topography and the scale. With small scale mapping, you don’t need much overlap because the mountains can’t cast their shadows very far. At large scales they can cast them many, many pixels.

Comparison of shaded relief produced for 1:1,000,000 (left), 1:150,000 (centre), and 1:30,000 (right).
Comparison of shaded relief produced for 1:1,000,000 (left), 1:150,000 (centre), and 1:30,000 (right).

To make tiling-with-overlap easier, I’ve written a bash script, called maketiles.sh, to tile large images, which you can download. This script uses the gdal_translate tool, so you also need to have GDAL installed.

Usage looks like this (assuming linux here):

./maketiles.sh <image file> <desired tile size in pixels> <number of columns in image> <number of rows in image> <tile_filename_prefix> <overlap>

It’s the rare DEM that can be cut perfectly into tiles without the last row and column being a bit off-sized. This script resolves that by increasing overlap on the final row and column so that all tiles are the requested size.

Let’s run through an example.

I have a DEM called 093L03-4-5-6_dem_26909.tif which is 4155 x 3572 pixels.

(If I don’t initially know its dimensions, I can get them through gdalinfo:

gdalinfo 093L03-4-5-6_dem_26909.tif
Driver: GTiff/GeoTIFF
Files: 093L03-4-5-6_dem_26909.tif
093L03-4-5-6_dem_26909.tfw
Size is 4155, 3572)

Let’s say I want tiles that are 2000×2000 pixels with a 100 pixel overlap, and I want the resulting tiles (and their world files) to be named tile_1_1.tif, tile_1_2.tif, etc.

The command would be

./maketiles.sh 093L03-4-5-6_dem_26909.tif 2000  4155 3572 tile 100

The script first echos back your parameters, and then tells you how many tiles will be produced. In this case we’ll be getting 6 tiles in 2 rows and 3 columns. It also tells you how much extra overlap you’ll have on the last row and column.

image file is 093L03-4-5-6_dem_26909.tif
desired tile size is  2000 x 2000
image is  4155 columns by  3572  rows
prefix is tile
overlap is 100 pixels.

Estimating 3 columns of tiles (X), by 2 rows of tiles (Y).

Taking a closer look at columns.
When laying out 3 columns of tiles, it looks like the final column will fall at 5800 pixels.
That's bigger than the image, so we'll back off the final column of tiles to begin at 2155 and end exactly at 4155.
This final column of tiles will overlap the second-to-last column by 1745 pixels.

Taking a closer look at rows.
When laying out 2 row of tiles, it looks like the final row will fall at 3900 pixels.
That's bigger than the image, so we'll back off the final row of tiles to begin at 1572 and end exactly at 3572.
This final row of tiles will overlap the second-to-last row by 428 pixels.

Layout has 6 tiles in 2 rows and 3 columns.

Finally, it computes the maximum overlap you could have without increasing the number of tiles, and asks if you want to try that.

Would you like to go with an overlap of 428, which would spread the overlap out but keep the same number of tiles? [Y/n]

If you accept this increased overlap, it confirms the change, and then you get a final chance to approve the process or exit.

Overlap set to 428.
overlap is 428.

Hit Enter to proceed with cutting tiles, or Ctrl-C to exit.

Assuming you hit Enter/Return, it then calls gdal_translate many times as is necessary, giving you extent info for each tile.

Producing tile tile_1_1.tif (row 1, column 1).  Extents: (0, 0) to (2000, 2000).
gdal_translate  -co "TFW=YES" -srcwin 0 0 2000 2000 093L03-4-5-6_dem_26909.tif tile_1_1.tif

Input file size is 4155, 3572
0...10...20...30...40...50...60...70...80...90...100 - done.

Producing tile tile_1_2.tif (row 1, column 2).  Extents: (1572, 0) to (3572, 2000).
gdal_translate  -co "TFW=YES" -srcwin 1572 0 2000 2000 093L03-4-5-6_dem_26909.tif tile_1_2.tif

Input file size is 4155, 3572
0...10...20...30...40...50...60...70...80...90...100 - done.

The final tile in the row will be a bit too big.
To keep all tiles the same size, I'm backing off the upper left corner of this tile from (3144,0) to (2155,0).

Producing tile tile_1_3.tif (row 1, column 3).  Extents: (2155, 0) to (4155, 2000).
gdal_translate  -co "TFW=YES" -srcwin 2155 0 2000 2000 093L03-4-5-6_dem_26909.tif tile_1_3.tif

Input file size is 4155, 3572
0...10...20...30...40...50...60...70...80...90...100 - done.
ROW IS FINISHED.

Producing tile tile_2_1.tif (row 2, column 1).  Extents: (0, 1572) to (2000, 3572).
gdal_translate  -co "TFW=YES" -srcwin 0 1572 2000 2000 093L03-4-5-6_dem_26909.tif tile_2_1.tif

Input file size is 4155, 3572
0...10...20...30...40...50...60...70...80...90...100 - done.

Producing tile tile_2_2.tif (row 2, column 2).  Extents: (1572, 1572) to (3572, 3572).
gdal_translate  -co "TFW=YES" -srcwin 1572 1572 2000 2000 093L03-4-5-6_dem_26909.tif tile_2_2.tif

Input file size is 4155, 3572
0...10...20...30...40...50...60...70...80...90...100 - done.

The final tile in the row will be a bit too big.
To keep all tiles the same size, I'm backing off the upper left corner of this tile from (3144,1572) to (2155,1572).

Producing tile tile_2_3.tif (row 2, column 3).  Extents: (2155, 1572) to (4155, 3572).
gdal_translate  -co "TFW=YES" -srcwin 2155 1572 2000 2000 093L03-4-5-6_dem_26909.tif tile_2_3.tif

Input file size is 4155, 3572
0...10...20...30...40...50...60...70...80...90...100 - done.
ROW IS FINISHED.
IMAGE IS FINISHED.

Tiles are named with the prefix you gave, followed by _X_Y.tif, where X is the row position, and Y the column position, of the tile. So in this case we got tile_1_1.tif, tile_1_2.tif, etc., along with tile_1_1.tfw, tile_1_2.tfw, etc

Mapping the travels of Charles Howard-Bury

[Update: an expanded version of this post has been published in Ripcord Adventure Journal, vol. 1, no. 1]

I’ve just finished a map for a book that had no map: Mountains of Heaven: Travels in the Tian Shan, 1913. This version of the Charles Howard-Bury’s journals from his travels in what are today eastern Kazakhstan and the adjacent region of China, was published in 1990 and although it’s out of print, it’s generally available in the used book market.

Here’s the map.

Charles Howard-Bury in the Tian Shan 1913 - map only - smaller

What I set out to do here was to resolve all the geographic references in the text. That is, you’re reading along, and he mentions the Yulduz plains or the village of Karachekinskaia, and you don’t know where that is. The various maps, atlases and website you can check don’t have it. So the book needs a supporting map. Pretty simple.

But it evolved into something else. Initially I planned a 24″x24″ sheet of paper, a size typical of folded maps that used to be glued inside the back covers of books. It would contain three maps. There would be a 1:1 million map detailing the central portion of his journey, the area where he spends the most time and mentions the most local details. There would be a 1:3 million map showing how he got from Semipalatinsk to Jarkent, the ten-day journey along Russian post roads for whose specific route he gives intriguingly few clues. Last, there would be a map at 1:7 million showing his exit route from Central Asia, from Jarkent to Tashkent, and then on by rail to the Caspian Sea.

Then things happened which illustrate some general hazards about mapping for old books.

The first map became the main project, and eventually pushed both other maps off the sheet. It’s what you see above. There were simply too many details to squeeze it in at a smaller scale onto part of the sheet. I let go of the idea that all the mapping one would need could fit into 24″x24″. I began building a hillshade of the area in Blender, and figured out an hypsographic colouring scheme inspired by an old Soviet topographic map of the area (К-44-Б): low elevations are white, and then rise through oranges and brown to white summits.

At the same time, as I tried to figure out just where Howard-Bury had gone, I found myself buried in old maps, wikipedia articles, and all sorts of other documents such as you get when you search on obscure terms like “Kuitun River.” I was reading maps in Russian and putting German websites through Google Translate. With each Aha! answer to a question, I felt more convinced that these puzzles — for the map had come to represent a series of puzzles — would make good stories.

At this point the map became a poster, the bottom half of which contained explanations of stuff. Here’s the poster:

Charles Howard-Bury in the Tian Shan, 1913_smaller

The sections in the bottom half cover the reason for the map, the plethora of alternate place names in Central Asia, a glossary of obscure terms Howard-Bury uses, an explanation of the Soviet topographic mapping system, some Turkic geographical equivalents, and a list of references. It is, in short, a brief tour of all the places I had to go to produce the map.

I’ll do another post here to present an example of one of the interesting puzzles: where was Lepzinsk?